To minimize system delays for FLchain, a new edge association solution is proposed in [1]} for digital twin-based edge networks. Here, digital twin is integrated into wireless networks in a fashion that IoT devices are mapped with twins in MEC servers. This potentially reduces communication latency and enhances reliability for the edge computing plane due to the reduction of the impact caused by unreliable communications between users and MEC servers. A blockchain platform running on a Delegated Proof of Stake (DPoS) [2]} consensus mechanism is adopted to establish the decentralized training network for FLchain in edge computing. Accordingly, the latency of the digital twin-based FLchain is formulated as the sum cost of local training on digital twins, model aggregation on BSs, transmission of model parameters, and block verification. A multi-agent deep reinforcement learning (DRL) algorithm is then proposed to solve the latency optimization problem with respect to learning accuracy conditions and bandwidth allocation constraints. Simulations based on a CIFAR10 dataset consisting of 60,000 images in 10 classes are performed in an edge network with 5 BSs and 100 end users, showing that the proposed scheme can achieve a lower system latency without compromising training performances.
